Motion Planning

Submitted by admin on Thu, 02/04/2010 - 23:12

Our work encompasses multiple research areas. We are investigating live
environment construction using 3D point clouds from scanning lasers and
stereo, where we model occlusions from the body parts of the robot.
Information from this 3D representation is also used to construct a
distance field that provides gradient information for moving away from
obstacles. Our ultimate goal is to use semantic information fused with
real-time sensor data to build more compact 3D representations for fast
collision checking and motion planning.

Another area of focus is in developing and implementing fast and novel motion planners. We are currently exploring
three different kinds of planners: probabilistic planners (through a
collaboration with Ioan Sucan and Lydia Kavraki at Rice), anytime
search-based planners (in collaboration with Maxim Likhachev and Ben
Cohen at Penn)
and trajectory optimizers based on the CHOMP algorithm developed at
the Intel/CMU Robotics Lab (implemented by Mrinal Kalakrishnan from USC
during his summer internship at Willow Garage). A major part of this
effort involves the creation of a planning infrastructure where
different types of planners can be easily plugged in. We also plan to
extend this infrastructure for whole-body planning and control of
robots with a mobile base and arms like the PR2.

We aim for safe physical contact interaction between robots and
untrained persons. In order to provide a reusable and extensible
system that simultaneously considers the motions of all limbs and allows for the inclusion of compliant
behavior, we are collaborating with Oussama Khatib's group at the
Stanford Robotics and AI lab to apply whole-body operational control to
the PR2.

We are also interested in planning and control in dynamically-changing
environments, especially in the presence of people. In this regard, we
are exploring the use of planners that specifically take into account
the motion of dynamic obstacles. We intend to integrate these planners
with perceptual models for moving obstacles and reactive controllers
that function at a more local level, and can safely execute desired
plans in the presence of people and other moving obstacles.

In addition to designing motion planners for completely autonomous
behavior, we are exploring the area of mixed autonomy where a
human in-the-loop
can make some decisions while being assisted by the motion planning
framework, e.g. in assisted tele-operation of a mobile base. Our system
would effectively serve as a guide or assistant, and actively prevent
unsafe behavior while still allowing the human operator to achieve
his or her desired tasks.